Abstract: With the rapid acceleration of global digitalization, the sixth-generation (6G) mobile network is poised to play a pivotal role in advancing industrial intelligence, fostering high-quality economic development, and enabling comprehensive societal digital transformation. Confronted with the increasing complexity and cost pressures of maintaining and optimizing existing fifth-generation (5G) mobile networks, as well as the limitations of added-on or patched artificial intelligence (AI), 6G networks must integrate AI into their design from the outset. On one hand, native AI can provide on-demand computing power, data, and algorithmic support, systematically enabling AI in the entire life cycle of the network. On the other hand, the digital twin (DT) of wireless networks further bolsters network simulation, dynamics prediction, and performance verification capabilities, tremendously reducing trial-and-error costs. Research on integrating native AI and DT technologies into 6G mobile networks is inspiring, and potential key technical benefits of the development of 6G network autonomy include:1. The architectures and deployment for 6G autonomous networksArchitectural design and deployment are crucial to better support autonomy in 6G networks. The service-based radio access network (RAN) architecture is a promising approach that leverages AI to enable dynamic resource allocation and hierarchical deployment, meeting diverse task requirements while improving network flexibility. Additionally, autonomous RAN frameworks that integrate AI with network DT (NDT) technologies can significantly enhance network autonomy, equipping networks with enhanced sensing, analysis, and optimization capabilities. Furthermore, with the rise of large model technologies, studying their deployment and optimization in 6G networks has emerged as a new focal point.2. DT-enabled AI learning and 6G network optimizationIntegrating AI and DT technologies brings intelligent enhancements to 6G networks while introducing new optimization challenges. The precise modeling and future network state prediction capabilities of DT networks (DTNs) not only enable high-fidelity reconstruction of communication environments but also enhance the network’s adaptability to dynamic changes. Furthermore, the collaborative optimization of pricing strategies and task offloading in 6G networks empowered by native AI and DT technologies can promote high-level network autonomy while optimizing the AI training process.3. Leveraging AI to enhance 6G network performanceAI technologies play a significant role in improving 6G network performance. By optimizing resource allocation and network configuration, AI can boost system capacity and significantly enhance communication efficiency. Additionally, integrating DT with AI facilitates the development of more efficient wireless network management solutions, fostering sustainable development in 6G networks.

人工智能赋能的数字孪生网络自治